Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)
| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot)
plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2)
tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F)
print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0032e+09 2 5.1521 0.006 **
## lag_depvar 2.6335e+11 1 2704.9149 <2e-16 ***
## Residuals 8.1781e+10 840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1809.409 16267.09 0.1457438
## 2-0 31308.177 23176.359 39439.99 0.0000000
## 2-1 24079.339 19374.068 28784.61 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
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## 782 44650.71 2 39438.43
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## 785 44134.14 2 38280.43
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## 789 45699.14 2 42564.29
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## 796 34230.57 2 40374.43
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## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
## 838 49195.00 2 59230.71
## 839 55436.43 2 49195.00
## 840 50353.14 2 55436.43
## 841 43194.86 2 50353.14
## 842 47539.71 2 43194.86
## 843 35271.00 2 47539.71
## 844 34774.86 2 35271.00
## 845 48788.71 2 34774.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 688 53542.44 22038.950
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2017.17965 4039.64070 -537.31104 2438.73114 -2968.12726 519.29288
## 8 9 10 11 12 13
## -5655.04433 -1188.60537 -3967.01515 -419.70359 -4941.19154 -1611.92922
## 14 15 16 17 18 19
## -902.23902 375.23276 -3244.53108 -380.07959 -2131.91789 6602.13661
## 20 21 22 23 24 25
## -1529.06304 -1208.47852 1475.24857 -1186.37985 234.65104 1695.22817
## 26 27 28 29 30 31
## -7101.20390 946.56518 8192.08149 420.55365 -11.53133 -2398.15157
## 32 33 34 35 36 37
## 1577.92888 4574.74114 1130.46160 2395.17004 -1863.75676 4611.41655
## 38 39 40 41 42 43
## 4305.81527 -2272.35212 -2979.09517 -1108.89699 -10740.64237 7287.06560
## 44 45 46 47 48 49
## 2557.28844 1367.60067 8106.22589 689.37960 6531.80916 6718.93076
## 50 51 52 53 54 55
## -5876.44261 -4791.88520 -5057.91639 -7928.35306 6127.82202 -4076.62828
## 56 57 58 59 60 61
## -4895.46456 3853.45399 886.84212 -32.73828 141.67242 -4996.87337
## 62 63 64 65 66 67
## 18125.01330 3644.17767 -3641.97193 5928.04310 7348.13998 14644.65515
## 68 69 70 71 72 73
## 1702.50825 -13203.93023 -1301.90246 4647.01315 -4895.82011 -4401.82255
## 74 75 76 77 78 79
## -10496.09767 2465.54360 -5400.00952 1062.37679 -6867.02267 544.70924
## 80 81 82 83 84 85
## -2356.19774 -2695.70330 -3933.97800 -540.14837 2312.33652 3761.33624
## 86 87 88 89 90 91
## 476.65028 -484.67146 196.36905 4301.76304 -1162.13011 1151.34655
## 92 93 94 95 96 97
## -2063.74551 -1044.13745 177.49220 274.76837 -7483.96449 2390.16352
## 98 99 100 101 102 103
## -8603.22797 -2942.96998 -4041.93393 -1739.47151 -1263.80636 3178.79322
## 104 105 106 107 108 109
## -2343.08932 2592.70868 -1158.89543 969.85292 2586.84157 -3153.98865
## 110 111 112 113 114 115
## -4723.33560 -851.44809 1902.40971 11693.08032 -1240.37293 2670.24714
## 116 117 118 119 120 121
## 4265.01558 3505.63267 -1096.46022 -4713.40896 -3722.45871 2320.51310
## 122 123 124 125 126 127
## -1731.35592 1341.29841 8859.43068 850.23142 133.49033 -2518.46161
## 128 129 130 131 132 133
## 2657.07205 7054.98127 1016.27920 -8495.71864 1750.60651 4137.21319
## 134 135 136 137 138 139
## -3161.46612 -1418.18546 -852.85692 -3879.12792 1183.05108 -495.11721
## 140 141 142 143 144 145
## -2913.31259 1717.86022 -1880.88931 -7829.47883 2037.65358 -3480.78422
## 146 147 148 149 150 151
## 2100.56293 -258.46157 1022.11140 -359.87840 1351.61358 1186.42372
## 152 153 154 155 156 157
## 3356.66017 -4860.97142 -1174.78880 -3236.30664 5955.61776 9747.00684
## 158 159 160 161 162 163
## -3643.29737 -4989.93640 3392.01835 -15.90386 2485.79333 -6120.44749
## 164 165 166 167 168 169
## -6958.30747 3945.00345 17181.25645 3411.12982 -616.45789 -2664.24651
## 170 171 172 173 174 175
## -1322.32779 3372.16305 -447.59283 -8295.10554 2645.02544 4105.76942
## 176 177 178 179 180 181
## 404.52324 8528.90342 -9472.31037 -3693.76330 -10966.23379 -11459.92155
## 182 183 184 185 186 187
## 1016.84463 9074.25373 -1652.06003 5706.21765 6329.80908 12928.33108
## 188 189 190 191 192 193
## 8192.63820 -4308.14351 2218.67493 10118.81035 -1900.87344 -2702.40953
## 194 195 196 197 198 199
## -10538.03428 -6615.60142 984.76154 -5482.40860 -10041.63095 5144.22845
## 200 201 202 203 204 205
## -3309.73541 -1951.54137 -1041.93261 6257.00458 9638.62229 325.30175
## 206 207 208 209 210 211
## 2670.12908 2840.73245 5524.77736 12570.12784 -5959.50558 -11562.51790
## 212 213 214 215 216 217
## -5922.86864 -10838.33419 -5317.14585 1289.45148 -13248.38712 16161.53811
## 218 219 220 221 222 223
## 7568.12614 1279.68821 26437.92008 12251.96342 7050.02256 13736.77431
## 224 225 226 227 228 229
## -4213.36730 -2034.32412 3488.73738 71.95161 2462.15653 8723.24185
## 230 231 232 233 234 235
## 5547.89550 -2186.24708 -2101.51187 9157.44967 -11780.81947 -7544.49345
## 236 237 238 239 240 241
## -8795.20592 -10348.20062 2838.87928 1111.21951 -8538.67892 -9224.45294
## 242 243 244 245 246 247
## 8866.60884 -8001.66443 2256.78255 -10534.66811 -4279.90434 1198.53066
## 248 249 250 251 252 253
## 775.78553 -12545.84487 3422.19763 1835.65801 3980.77510 1897.67180
## 254 255 256 257 258 259
## -1401.52284 10897.61777 20626.01734 2916.95538 -4555.52341 3830.15384
## 260 261 262 263 264 265
## -1977.83363 3456.83534 -5135.03150 -11170.23576 -4992.34215 -779.95145
## 266 267 268 269 270 271
## -5446.07582 8525.51514 -4544.97926 3928.82816 -2373.94066 4165.50207
## 272 273 274 275 276 277
## 436.96156 7029.22779 -1696.39728 11742.24464 -4885.23230 1430.45969
## 278 279 280 281 282 283
## -669.43152 7555.51361 -5364.27141 -3028.12697 -11551.66898 -2939.16392
## 284 285 286 287 288 289
## 18390.25145 7488.27727 2426.84329 -938.75991 598.95890 6091.60247
## 290 291 292 293 294 295
## 6566.73990 -19097.25208 -11424.02806 -8381.68423 9422.48702 2811.99124
## 296 297 298 299 300 301
## -1444.15717 27140.04740 9749.30633 4568.11795 9180.45173 2505.79607
## 302 303 304 305 306 307
## -1381.75311 7556.62615 -24644.24630 -3822.74196 -450.38255 -7238.72379
## 308 309 310 311 312 313
## -4223.23253 2692.21982 -9436.89530 -3451.84215 -8399.48796 1371.34471
## 314 315 316 317 318 319
## -3350.40483 1854.26627 -4284.54997 27249.48180 -1008.57091 3008.87117
## 320 321 322 323 324 325
## 10540.97096 5275.56118 32057.90074 4718.62140 -21327.01209 1467.11101
## 326 327 328 329 330 331
## 788.85603 -6781.57833 -2027.42855 -33550.74280 722.76107 -2461.73516
## 332 333 334 335 336 337
## -245.55741 -3320.11295 3940.78133 -595.07153 -7110.98625 -3257.73645
## 338 339 340 341 342 343
## -2327.13666 -7812.37747 3736.05963 -1503.64879 -1871.21475 -1127.09879
## 344 345 346 347 348 349
## 41.50146 341.23980 -1765.08831 -9593.31286 -13335.19425 2215.53039
## 350 351 352 353 354 355
## -4433.15084 -3763.41046 -6082.02235 1657.75131 1276.43577 2631.74434
## 356 357 358 359 360 361
## -3905.15279 -651.37829 536.29253 6863.47815 98.26849 -221.48060
## 362 363 364 365 366 367
## 2396.56105 -2947.54766 -1066.73994 -8930.69517 -4784.63417 -6357.73820
## 368 369 370 371 372 373
## -5077.42625 -7368.88924 4916.31854 246.14896 6986.11136 -7800.71309
## 374 375 376 377 378 379
## -2404.98105 -3526.55704 -2598.66357 -12585.70772 1810.68207 -10741.74668
## 380 381 382 383 384 385
## 5615.99197 9225.19155 2973.28665 -2568.87199 1437.82517 6566.62971
## 386 387 388 389 390 391
## 11204.79832 -6052.48248 -5596.07402 -374.62522 8344.54152 1563.78869
## 392 393 394 395 396 397
## 10963.95972 -10176.52193 2512.00750 441.47961 290.53339 -925.44732
## 398 399 400 401 402 403
## -829.99284 -14749.78891 8320.78762 -1409.56089 -1593.36209 6768.45977
## 404 405 406 407 408 409
## -8170.29177 -1500.96912 -2727.65876 -6002.82153 -3018.41018 -4065.48688
## 410 411 412 413 414 415
## -8890.11199 6028.62235 1505.40244 -7520.09716 -7814.97787 14121.76304
## 416 417 418 419 420 421
## 3650.70816 4303.84237 -8246.79698 -4924.52202 -2763.32979 2666.48517
## 422 423 424 425 426 427
## -14177.76424 -2906.18887 -9207.80703 2932.89093 6876.00082 6437.89430
## 428 429 430 431 432 433
## -4157.52459 -4278.48963 -4866.99814 -1919.98960 -5840.31397 -6738.62095
## 434 435 436 437 438 439
## -6043.19322 -1472.77260 -931.82121 -5065.60475 2500.62035 4738.14478
## 440 441 442 443 444 445
## -5185.63868 -2274.35714 1461.37988 -3965.23967 2715.97156 -6715.08781
## 446 447 448 449 450 451
## -12227.82157 -4590.36141 9573.53711 -2148.72311 4639.18849 -6008.10196
## 452 453 454 455 456 457
## -1243.65031 261.81016 2897.76224 -12412.55896 3265.30701 -6823.74947
## 458 459 460 461 462 463
## 6418.32087 2878.44738 2358.16163 -4006.58428 1943.82090 -166.41490
## 464 465 466 467 468 469
## 1632.03726 -690.26953 3182.16490 -2820.37777 5634.10651 -7133.44363
## 470 471 472 473 474 475
## -3127.88515 -2357.20989 -4807.68364 2868.21148 7655.80901 -6188.07534
## 476 477 478 479 480 481
## 1335.23477 -6333.83956 -2978.01340 1886.24799 -13065.47456 -9849.62334
## 482 483 484 485 486 487
## -1270.70023 -53.02279 -1044.18084 -1428.54781 -9674.47987 11029.76830
## 488 489 490 491 492 493
## 6123.18076 7281.77834 -5603.29440 5218.47759 9124.07335 5853.12622
## 494 495 496 497 498 499
## -13692.27580 -10734.01524 -3572.18572 -1227.01688 -644.52792 -7747.31502
## 500 501 502 503 504 505
## 512.64570 4182.68287 5385.07481 515.47665 -67.67488 -7388.24539
## 506 507 508 509 510 511
## 444.28061 -5178.22270 1717.25826 -1421.10795 -8280.91851 -701.38787
## 512 513 514 515 516 517
## -2776.83744 -686.21218 1230.36835 -9606.41739 -7850.18322 24219.49283
## 518 519 520 521 522 523
## 9670.43483 5691.03961 -5541.72682 2613.07869 16826.21742 11226.50378
## 524 525 526 527 528 529
## -24427.38491 -5248.63862 -3902.62702 4416.25675 -528.15253 -11272.16820
## 530 531 532 533 534 535
## 4259.46782 13760.89268 -5172.85858 4196.19336 5363.47674 -1998.69261
## 536 537 538 539 540 541
## -4739.81653 -7254.54401 -2255.12123 8175.35754 -47.97212 -8315.52013
## 542 543 544 545 546 547
## 1670.47374 -751.72014 215.90920 -11182.82163 -11177.53714 1957.63812
## 548 549 550 551 552 553
## 6907.27562 -1442.22703 713.67553 -7849.73384 8458.64927 767.72672
## 554 555 556 557 558 559
## -12088.21926 9055.47470 8515.10568 -75.13043 4678.53509 -3764.94608
## 560 561 562 563 564 565
## 13931.20061 21274.75603 -6758.34935 -9940.95273 6555.10723 -11.48682
## 566 567 568 569 570 571
## 3222.64704 -7614.48024 -17512.89697 6522.14788 6273.15014 1721.73602
## 572 573 574 575 576 577
## 2914.90603 1579.98955 -2356.65652 14536.62520 -9875.61964 -6434.20925
## 578 579 580 581 582 583
## 8545.10119 2664.90517 -6744.86767 7335.13608 -3995.97710 -2955.19412
## 584 585 586 587 588 589
## 15534.95471 -14716.62395 8262.90067 -120.67735 -6399.59711 -916.96645
## 590 591 592 593 594 595
## 93.90561 -10810.28915 1675.97911 -7272.40933 2961.79633 8747.67071
## 596 597 598 599 600 601
## -7650.12168 5726.07074 2588.83166 6700.65583 -3366.64157 5985.94337
## 602 603 604 605 606 607
## -8479.84962 2103.98162 1112.62270 2978.54364 1327.43334 227.65090
## 608 609 610 611 612 613
## -5978.52315 7928.13222 -1354.49392 -2737.51796 -3605.31936 -8367.00563
## 614 615 616 617 618 619
## 11846.74970 4786.20213 -9478.07672 11494.54807 5882.89785 -5754.75520
## 620 621 622 623 624 625
## 26200.67683 -13058.33069 -6961.60575 3002.17467 -4320.10970 -10736.84903
## 626 627 628 629 630 631
## 11185.37321 -21780.51345 -2496.20116 8596.85767 11028.26475 -1693.14746
## 632 633 634 635 636 637
## 33149.73195 -6814.41842 5518.93675 5192.41988 -2481.92232 -5542.67940
## 638 639 640 641 642 643
## -2114.60521 -12594.53296 -2367.02373 -2004.15455 -2633.13240 -2964.48898
## 644 645 646 647 648 649
## 1715.17459 4323.98412 16844.19371 18384.99549 667.41000 4578.68686
## 650 651 652 653 654 655
## 10396.18962 19915.16703 470.24132 -28321.12400 -1507.63360 -2445.82356
## 656 657 658 659 660 661
## 1727.00044 -3331.79410 -10747.55350 1569.31240 4127.64710 -1114.93327
## 662 663 664 665 666 667
## 12928.02331 1226.04114 1679.77118 -11825.18741 1275.85843 1082.17244
## 668 669 670 671 672 673
## -5272.15649 -7502.41932 1992.07609 -3791.58676 2601.06905 -3458.62813
## 674 675 676 677 678 679
## -9410.02652 -8362.67080 -3023.44939 125.90370 2793.72318 649.22587
## 680 681 682 683 684 685
## -3895.51559 -1873.00478 -1382.87720 -8308.07731 4595.04249 -2309.71899
## 686 687 688 689 690 691
## -1464.58834 520.43315 10782.35091 9755.76802 10512.51423 -9788.93770
## 692 693 694 695 696 697
## -3655.68660 -3232.27414 5785.41511 -10480.71851 -7986.33280 -8673.02513
## 698 699 700 701 702 703
## -6324.39820 -4783.47391 3040.13014 -4454.27049 -1947.83913 4171.07418
## 704 705 706 707 708 709
## 31045.22269 9435.35133 23363.29466 1602.59944 8254.23182 22858.51601
## 710 711 712 713 714 715
## 6506.13766 -18248.77999 4787.04422 -5475.40987 -130.29126 450.72003
## 716 717 718 719 720 721
## -17295.38596 -5292.61623 3306.46777 -3041.89947 -13006.69977 4251.84587
## 722 723 724 725 726 727
## -5584.40173 714.29571 -3963.09567 -12475.72329 1339.37109 -1895.62668
## 728 729 730 731 732 733
## -9806.29035 17240.27155 1745.58159 -2752.59146 5685.46717 -8660.49820
## 734 735 736 737 738 739
## -752.91320 8108.81818 -15381.33344 -5940.21832 7378.99335 -4814.22526
## 740 741 742 743 744 745
## 131.16417 1797.88890 -1985.88482 -5198.07432 6382.73818 -6305.20330
## 746 747 748 749 750 751
## 22668.73280 7797.00862 -1976.67701 -7317.54587 23387.43512 -4318.67417
## 752 753 754 755 756 757
## 1369.69124 -14448.13449 56082.72426 26904.44860 15087.38924 -10648.12634
## 758 759 760 761 762 763
## 10604.83343 7315.22137 5811.89270 -46385.57403 -16176.83249 956.95637
## 764 765 766 767 768 769
## -2526.62320 -3467.37365 122833.82414 19345.64310 43759.39844 22638.61277
## 770 771 772 773 774 775
## 12132.99106 15902.74023 25782.95399 -98749.44661 -6744.17145 -35823.25024
## 776 777 778 779 780 781
## 1729.99204 -1234.66785 3390.22813 -7418.68728 -1451.49316 -1951.61662
## 782 783 784 785 786 787
## 3418.03865 -7159.32018 -2243.14810 3913.07514 2261.66160 -2760.92878
## 788 789 790 791 792 793
## -4049.65486 1735.77478 2851.93438 -50.90309 -6702.31574 -5783.72572
## 794 795 796 797 798 799
## -1150.33850 -1265.83545 -7819.77693 -2359.04268 -3273.35642 -2671.17989
## 800 801 802 803 804 805
## 10718.57123 2241.02827 7080.95819 2928.10198 -5442.28569 8191.28419
## 806 807 808 809 810 811
## 9882.10583 -10590.76953 -7389.54871 -7501.04044 3007.49874 4197.68297
## 812 813 814 815 816 817
## -2264.08402 -14160.07103 -4110.74797 6258.62504 8239.46074 -9667.05676
## 818 819 820 821 822 823
## -7747.17969 -9335.58131 9751.95280 -1220.36921 -4369.11524 -8445.32720
## 824 825 826 827 828 829
## 7873.90956 7916.94763 4981.61239 -3094.53595 -705.30235 2898.28839
## 830 831 832 833 834 835
## 4675.69808 1632.78090 -6685.46138 2094.75952 -391.83787 2759.46853
## 836 837 838 839 840 841
## 4147.36287 -1128.78900 -9327.85959 5680.58804 -4855.09714 -7572.71611
## 842 843 844 845
## 3025.49038 -13038.81277 -2817.22803 11630.05057
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17252.11 20099.36 24353.45 24071.41 26424.84 23757.42 24473.76 19705.75
## 10 11 12 13 14 15 16 17
## 19442.30 16784.99 17562.48 14291.79 14342.95 15007.62 16704.25 15024.22
## 18 19 20 21 22 23 24 25
## 16058.92 15432.43 22515.06 21599.05 21078.89 22968.95 22294.92 22947.49
## 26 27 28 29 30 31 32 33
## 24793.49 18721.72 20447.92 28285.45 28343.10 28016.01 25645.36 27047.83
## 34 35 36 37 38 39 40 41
## 30890.97 31239.40 32648.61 30159.15 34137.18 37345.35 34401.38 31212.18
## 42 43 44 45 46 47 48 49
## 30059.93 20639.22 28158.14 30594.69 31683.92 38522.19 38016.76 42679.07
## 50 51 52 53 54 55 56 57
## 46915.44 39613.17 34181.49 29204.07 22348.32 28638.49 25219.04 21516.55
## 58 59 60 61 62 63 64 65
## 25925.02 27184.60 27481.61 27893.44 23764.27 40355.97 42199.97 37445.81
## 66 67 68 69 70 71 72 73
## 41652.86 46568.63 57237.06 55250.79 40493.62 37999.42 41017.39 35317.39
## 74 75 76 77 78 79 80 81
## 30769.53 21472.74 24674.30 20599.91 22686.02 17581.43 19596.91 18823.42
## 82 83 84 85 86 87 88 89
## 17851.12 15920.01 17197.81 20805.95 25223.78 26213.67 26238.63 26855.38
## 90 91 92 93 94 95 96 97
## 30980.56 29811.08 30810.46 28874.85 28074.65 28442.80 28849.39 22426.69
## 98 99 100 101 102 103 104 105
## 25441.80 18472.11 17328.22 15368.90 15668.66 16346.06 20818.80 19902.29
## 106 107 108 109 110 111 112 113
## 23413.47 23203.43 24879.59 27756.42 25254.48 21697.88 21973.30 24619.63
## 114 115 116 117 118 119 120 121
## 35484.37 33677.18 35514.70 38513.08 40469.03 38157.41 32978.32 29319.63
## 122 123 124 125 126 127 128 129
## 31402.50 29682.42 30864.00 38463.91 38106.37 37167.89 34031.36 35812.59
## 130 131 132 133 134 135 136 137
## 41210.58 40650.86 31852.39 33117.22 36307.04 32717.61 31104.86 30189.84
## 138 139 140 141 142 143 144 145
## 26746.81 28161.26 27930.88 25617.14 27641.60 26266.34 19868.35 22898.93
## 146 147 148 149 150 151 152 153
## 20725.58 23702.75 24242.75 25833.16 26015.24 27669.43 28970.20 32002.40
## 154 155 156 157 158 159 160 161
## 27472.50 26735.45 24290.67 30184.85 41663.73 39993.94 37358.84 42379.19
## 162 163 164 165 166 167 168 169
## 43787.78 47203.73 42669.59 37976.71 43402.03 59704.44 61916.60 60330.68
## 170 171 172 173 174 175 176 177
## 57156.33 55555.55 58258.16 57282.25 49574.26 52397.80 56140.48 56176.67
## 178 179 180 181 182 183 184 185
## 63305.60 53807.76 50558.66 41367.21 32906.44 36414.75 46518.35 45974.35
## 186 187 188 189 190 191 192 193
## 51927.19 57672.24 68455.36 73738.29 67432.90 67626.33 74696.73 70373.12
## 194 195 196 197 198 199 200 201
## 65895.89 55139.60 49169.67 50593.98 46188.63 38357.34 44782.16 43009.54
## 202 203 204 205 206 207 208 209
## 42647.50 43125.85 49919.95 58809.27 58438.87 60163.70 61819.51 65610.73
## 210 211 212 213 214 215 216 217
## 75077.36 67160.09 55349.01 49957.76 40954.00 37911.69 41025.39 31045.46
## 218 219 220 221 222 223 224 225
## 48019.16 55340.03 56241.94 79007.61 86502.69 88505.94 96097.37 87048.18
## 226 227 228 229 230 231 232 233
## 81046.55 80628.48 77278.41 76439.90 81176.96 82541.25 76976.65 72189.55
## 234 235 236 237 238 239 240 241
## 77843.25 64490.92 56527.35 48477.91 40089.41 44281.35 46434.11 39884.74
## 242 243 244 245 246 247 248 249
## 33564.25 43846.81 38093.65 42029.38 34293.19 32999.04 36654.36 39478.27
## 250 251 252 253 254 255 256 257
## 30307.66 36245.77 40047.22 45242.04 47960.38 47452.95 57753.98 75251.33
## 258 259 260 261 262 263 264 265
## 75066.38 68376.99 69858.83 66079.59 67525.75 61283.38 50557.91 46585.24
## 266 267 268 269 270 271 272 273
## 46794.65 42901.34 51705.55 47978.60 52125.37 50241.93 54309.32 54605.34
## 274 275 276 277 278 279 280 281
## 60622.83 58257.04 67930.09 61854.83 62064.86 60413.91 66156.84 59887.27
## 282 283 284 285 286 287 288 289
## 56451.10 46003.31 44400.03 61632.44 67162.59 67572.05 64989.61 64076.97
## 290 291 292 293 294 295 296 297
## 68077.97 71988.25 52984.60 43086.54 37097.51 47419.01 50660.87 49774.81
## 298 299 300 301 302 303 304 305
## 73971.41 79916.88 80584.55 85197.06 83395.61 78425.80 81892.67 56791.17
## 306 307 308 309 310 311 312 313
## 53052.24 52732.01 46522.09 43731.49 47334.90 39886.99 38609.06 33170.51
## 314 315 316 317 318 319 320 321
## 36955.12 36136.45 39967.98 37952.38 63739.14 61580.27 63203.89 71202.15
## 322 323 324 325 326 327 328 329
## 73589.53 99071.66 97449.30 73279.03 72076.86 70434.15 62385.71 59507.89
## 330 331 332 333 334 335 336 337
## 29455.67 33143.31 33582.84 35902.83 35243.65 41010.79 42086.41 37333.88
## 338 339 340 341 342 343 344 345
## 36548.28 36674.95 31993.80 37992.93 38656.36 38914.81 39790.64 41576.62
## 346 347 348 349 350 351 352 353
## 43398.66 43150.31 36094.77 26662.33 32007.15 30868.12 30458.17 28074.53
## 354 355 356 357 358 359 360 361
## 32753.56 36507.97 40971.72 39160.66 40420.99 42559.52 49955.02 50505.62
## 362 363 364 365 366 367 368 369
## 50707.30 53170.55 50653.88 50098.41 42743.35 39940.02 36116.85 33895.46
## 370 371 372 373 374 375 376 377
## 29953.11 37241.28 39528.32 47414.14 41385.55 40832.70 39369.95 38902.71
## 378 379 380 381 382 383 384 385
## 29770.03 34368.32 27419.72 35639.38 45972.86 49538.44 47811.75 49803.51
## 386 387 388 389 390 391 392 393
## 56023.92 65509.77 58720.79 53188.77 52917.46 60297.35 60820.75 69489.81
## 394 395 396 397 398 399 400 401
## 58594.99 60161.95 59722.04 59205.88 57692.71 56454.22 43212.21 51798.28
## 402 403 404 405 406 407 408 409
## 50798.65 49764.83 56166.43 48708.54 48019.66 46346.25 42023.27 40853.92
## 410 411 412 413 414 415 416 417
## 38917.68 33011.52 40884.74 43811.24 38483.26 33571.24 48443.72 52288.73
## 418 419 420 421 422 423 424 425
## 56218.23 48686.95 45010.04 43685.94 47272.62 35691.05 35420.24 29678.68
## 426 427 428 429 430 431 432 433
## 35268.86 43596.96 50489.52 47254.78 44323.28 41248.28 41136.46 37614.05
## 434 435 436 437 438 439 440 441
## 33752.19 30986.06 32562.25 34411.75 32416.24 37282.71 43488.64 40240.79
## 442 443 444 445 446 447 448 449
## 39946.76 42953.38 40839.31 44829.09 40075.68 31107.36 29944.75 41302.44
## 450 451 452 453 454 455 456 457
## 40983.95 46635.53 42271.36 42621.05 44241.67 47960.13 37833.69 42683.32
## 458 459 460 461 462 463 464 465
## 38106.25 45675.84 49196.12 51816.87 48546.18 50887.13 51088.68 52835.84
## 466 467 468 469 470 471 472 473
## 52333.41 55277.38 52605.46 57657.02 50916.46 48527.21 47113.26 43737.36
## 474 475 476 477 478 479 480 481
## 47493.76 54957.65 49384.19 51087.55 45876.01 44254.89 47088.05 36501.48
## 482 483 484 485 486 487 488 489
## 30062.56 31932.02 34628.90 36118.98 37084.91 30725.23 43256.39 49917.08
## 490 491 492 493 494 495 496 497
## 56747.87 51458.95 56292.36 63926.59 67738.28 53993.59 44570.76 42595.59
## 498 499 500 501 502 503 504 505
## 42918.81 43710.03 38196.35 40595.46 45897.35 51579.38 52289.10 52399.67
## 506 507 508 509 510 511 512 513
## 46101.15 47441.22 43700.17 46455.82 46121.49 39836.82 40967.98 40143.07
## 514 515 516 517 518 519 520 521
## 41248.77 43888.99 36728.61 32007.65 55898.99 64060.25 67713.44 61092.06
## 522 523 524 525 526 527 528 529
## 62431.64 76018.21 82995.38 57943.92 52813.63 49507.74 53887.01 53393.31
## 530 531 532 533 534 535 536 537
## 43576.25 48568.39 61229.72 55750.24 59148.09 63136.12 60188.53 55218.97
## 538 539 540 541 542 543 544 545
## 48680.84 47336.64 55274.26 55024.66 47584.24 49808.01 49634.66 50328.54
## 546 547 548 549 550 551 552 553
## 40976.97 32812.22 37154.30 45271.37 45068.32 46774.31 40783.78 49797.27
## 554 555 556 557 558 559 560 561
## 50952.65 40731.24 50272.75 58135.99 57500.89 61098.80 56865.80 68626.96
## 562 563 564 565 566 567 568 569
## 85316.49 75406.95 63969.89 68389.34 66513.64 67700.34 59269.90 43258.14
## 570 571 572 573 574 575 576 577
## 50267.14 56172.55 57355.38 59431.01 60078.09 57204.37 69451.62 58824.49
## 578 579 580 581 582 583 584 585
## 52547.18 60149.09 61653.15 54746.86 61013.69 56589.62 53634.05 67204.77
## 586 587 588 589 590 591 592 593
## 52632.67 59977.25 59069.60 52791.54 52096.67 52372.72 43088.16 45885.12
## 594 595 596 597 598 599 600 601
## 40511.35 44757.33 53520.98 46851.93 52711.17 55089.06 60758.36 56916.34
## 602 603 604 605 606 607 608 609
## 61730.28 53298.59 55178.66 55955.03 58263.28 58837.35 58378.09 52555.30
## 610 611 612 613 614 615 616 617
## 59617.21 57677.23 54774.32 51480.29 44442.96 55953.66 59841.22 50776.31
## 618 619 620 621 622 623 624 625
## 61178.67 65363.76 58853.32 81081.62 66203.89 58532.97 60535.97 55889.13
## 626 627 628 629 630 631 632 633
## 46224.20 56931.94 37487.63 37347.86 46916.45 57399.43 55443.98 84173.85
## 634 635 636 637 638 639 640 641
## 74359.78 76560.58 78197.92 72924.11 65643.18 62277.39 50182.02 48550.30
## 642 643 644 645 646 647 648 649
## 47441.85 45924.06 44308.68 46985.59 51603.09 66574.29 80998.88 78122.17
## 650 651 652 653 654 655 656 657
## 79025.95 84897.55 98342.47 93100.98 63370.49 60822.25 57776.57 58761.22
## 658 659 660 661 662 663 664 665
## 55202.12 45614.69 47999.07 52316.93 51509.12 63071.10 62948.80 63238.33
## 666 667 668 669 670 671 672 673
## 51693.57 53053.11 54071.59 49410.28 43389.92 46424.87 44023.65 47510.49
## 674 675 676 677 678 679 680 681
## 45262.88 38100.39 32758.31 32755.81 35504.85 40236.92 42497.37 40501.86
## 682 683 684 685 686 687 688 689
## 40525.45 40974.22 35316.53 41646.00 41143.45 41442.71 43438.22 54146.09
## 690 691 692 693 694 695 696 697
## 62603.49 70652.79 59949.54 55957.27 52839.58 57993.72 48286.48 41985.45
## 698 699 700 701 702 703 704 705
## 35881.11 32600.19 31080.16 36586.84 34850.41 35523.07 41456.06 70115.79
## 706 707 708 709 710 711 712 713
## 76274.42 93821.69 90140.91 92736.20 107761.43 106602.07 83963.81 84311.12
## 714 715 716 717 718 719 720 721
## 75649.43 72752.14 70728.67 53458.33 48856.68 52348.76 49853.56 38968.73
## 722 723 724 725 726 727 728 729
## 44536.69 40807.99 43053.10 40928.29 31635.63 35586.34 36211.58 29847.16
## 730 731 732 733 734 735 736 737
## 47914.70 50162.31 48196.25 53850.07 46256.77 46531.32 54512.62 40964.36
## 738 739 740 741 742 743 744 745
## 37376.44 45877.51 42652.12 44154.68 46923.31 46036.50 42455.69 49444.35
## 746 747 748 749 750 751 752 753
## 44465.55 65427.28 70747.39 66856.83 58792.42 78570.82 71645.31 70564.56
## 754 755 756 757 758 759 760 761
## 55802.28 104520.69 121590.61 126179.41 107706.02 110134.21 109381.68 107411.00
## 762 763 764 765 766 767 768 769
## 60090.69 45142.33 47051.48 45676.09 43652.75 152219.64 156656.32 181859.53
## 770 771 772 773 774 775 776 777
## 185425.87 179363.83 177361.33 184243.16 81465.74 72055.39 38431.72 41864.52
## 778 779 780 781 782 783 784 785
## 42273.49 46670.97 41070.06 41390.05 41232.68 45786.03 40523.58 40221.07
## 786 787 788 789 790 791 792 793
## 45334.77 48359.36 46613.94 43963.37 46701.92 50069.33 50475.17 45019.15
## 794 795 796 797 798 799 800 801
## 41055.34 41640.26 42050.35 36683.19 36764.93 36037.61 35928.29 47529.83
## 802 803 804 805 806 807 808 809
## 50258.90 56871.04 59019.43 53584.00 60745.75 68479.20 57350.26 50424.75
## 810 811 812 813 814 815 816 817
## 44277.36 48087.17 52455.08 50625.93 38635.89 36940.52 44517.97 52867.91
## 818 819 820 821 822 823 824 825
## 44519.47 38903.58 32610.05 43786.65 43965.12 41370.33 35542.66 44707.91
## 826 827 828 829 830 831 832 833
## 52752.10 57215.11 54058.73 53388.57 55951.16 59742.50 60396.32 53700.81
## 834 835 836 837 838 839 840 841
## 55521.98 54940.67 57185.78 60359.50 58522.86 49755.84 55208.24 50767.57
## 842 843 844 845
## 44514.22 48309.81 37592.09 37158.66
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8095
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.152072 0.7725102 3.916305
## t2* 2704.914897 164.0228152 887.993715
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.200385 5.091398 13.42153
## 2 lag_depvar 1655.759768 2746.176312 4498.82791
Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jul 28 01:03:49 2025
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## =-=-=-=-= Iteration 2000 Mon Jul 28 01:03:59 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jul 28 01:04:09 2025
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## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 28 01:04:38 2025
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## =-=-=-=-= Iteration 12000 Mon Jul 28 01:04:47 2025
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## =-=-=-=-= Iteration 18000 Mon Jul 28 01:05:16 2025
## =-=-=-=-=
#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 9.573500 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 208.570167 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 54.943000 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.198333 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 27.625000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 17.414833 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 18.330000 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 338.654833 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2777, min uf: 26799.01, min date: 2018-01-01
#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
## Warning: Removed 2777 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
##
## invoke
## The following object is masked from 'package:data.table':
##
## :=
model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-08-09 00:04:58 sería de: 26.443 pesos// Percentil 95% más alto proyectado: 34.867,93
Según TimeGPT: La proyección de la UF a 298 días más 2026-06-03 sería de: 39.627,14 pesos// Percentil 80% más alto proyectado: 39.944,44 pesos// Percentil 95% más alto proyectado: 40.252,43
Según prophet: La proyección de la UF a 298 días más 2026-06-03 sería de: 42.418 pesos// Percentil 95% más alto proyectado: 51.691
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26067.78 | 26331.96 |
| Lo.80 | 26196.95 | 26493.62 |
| Point.Forecast | 26442.72 | 26799.01 |
| Hi.80 | 31244.83 | 32070.87 |
| Hi.95 | 34130.49 | 34861.62 |
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t
Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4645 1032.7501
## s.e. 0.1027 40.7271
##
## sigma^2 = 38285: log likelihood = -514.65
## AIC=1035.3 AICc=1035.63 BIC=1042.33
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4565 793.7058 7.3274
## s.e. 0.1034 343.3093 10.4418
##
## sigma^2 = 38563: log likelihood = -514.41
## AIC=1036.81 AICc=1037.37 BIC=1046.19
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 611.2196 | 599.6667 | 501.7880 |
| Lo.80 | 760.9573 | 749.5620 | 570.1602 |
| Point.Forecast | 1043.8185 | 1032.7210 | 725.7628 |
| Hi.80 | 1326.6798 | 1315.8799 | 1073.8713 |
| Hi.95 | 1476.4174 | 1465.7753 | 1321.3814 |
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.3 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.9.0 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.6
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.1.2
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.5 MASS_7.3-60.2
## [10] crosstalk_1.2.1 magrittr_2.0.3 sass_0.4.10
## [13] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.11
## [31] spatial_7.3-17 parallelly_1.45.0 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 farver_2.1.2
## [37] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [40] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [43] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.3
## [46] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.17
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [64] networkD3_0.4.1 gtools_3.9.5 generics_0.1.4
## [67] htmlwidgets_1.6.4 ggstats_0.9.0 pkgconfig_2.0.3
## [70] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [73] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [76] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [79] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
## [82] uuid_1.2-1 nlme_3.1-164 curl_6.4.0
## [85] cachem_1.1.0 KernSmooth_2.23-22 parallel_4.4.0
## [88] fBasics_4041.97 pillar_1.10.2 vctrs_0.6.5
## [91] gplots_3.2.0 slam_0.1-55 car_3.1-3
## [94] dbplyr_2.5.0 xtable_1.8-4 evaluate_1.0.4
## [97] mvtnorm_1.3-3 cli_3.6.5 compiler_4.4.0
## [100] crayon_1.5.3 rngtools_1.5.2 future.apply_1.20.0
## [103] labeling_0.4.3 rstan_2.32.7 QuickJSR_1.8.0
## [106] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [109] Matrix_1.7-0 hms_1.1.3 bit64_4.6.0-1
## [112] future_1.58.0 nixtlar_0.6.2 extraDistr_1.10.0
## [115] igraph_2.1.4 RcppParallel_5.1.10 bslib_0.9.0
## [118] quantmod_0.4.28 bit_4.6.0
#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))